Population Pharmacokinetic Characteristics of Levosulpiride and Terbinafine in Healthy Male Korean Volunteers Yong-Bok Lee College of Pharmacy and Institute of Bioequivalence and Bridging Study, Chonnam National University, Gwangju , Korea.

INTRODUCTION  The purposes of this study were to assess the population pharmacokinetics of levosulpiride and terbinafine in healthy male Korean subjects using a population approach and to investigate the influence of characteristics of subjects such as age, body weight, height and serum creatinine concentration on that of levosulpiride and terbinafine, respectively.

SUBJECTS AND DATA COLLECTION (1)  Serum Data from 192 healthy male Korean subjects were used for levosulpiride analysis.  After overnight fast, each subject received a single 25 ㎎ oral dose of levosulpiride. Blood samples were collected for 0~36 hours.  Informed consent was obtained from the subjects after explaining the nature and purpose details of the study.  Levosulpiride serum concentrations were measured using HPLC with fluorescence detection.

SUBJECTS AND DATA COLLECTION (2)  Serum Data from 73 healthy male Korean subjects were used for terbinafine analysis.  After overnight fast, each subject received a single 125 ㎎ oral dose of terbinafine. Blood samples were collected for 0~60 hours.  Informed consent was obtained from the subjects after explaining the nature and purpose details of the study.  Terbinafine serum concentrations were measured using HPLC with UV detection.

PHARMACOKINETIC MODEL EVALUATION  Two pharmacokinetic (PK) models were evaluated and compared for population approach using WinNonlin. A.1-compartment model without lag time B.1-compartment model with lag time C.2-compartment model without lag time D.2-compartment model with lag time Afterwards, Akaike’s Information Criteria (AIC), condition number, frequency of changing +/– signs (runs) and CV% were mainly used to determine the model that most adequately described the levosulpiride and terbinafine data.

AKAIKE’S INFORMATION CRITERIA A measure of goodness of fit based on maximum likelihood. When comparing several models for a given set of data, the model associated with the smallest value of AIC is regarded as giving the best fit out of that set of models. AIC is only appropriate for use when comparing models using the same weighting scheme. N : number of observations. R : residual sum of squares. P : number of parameters.

STANDARD TWO STAGE (STS) METHOD First stage Total 192 and 73 subjects were analyzed individually - according to optimal model - by non-compartmental methods WinNonlin was used for STS method. Second stage Individual PK parameters - mean ± standard deviation

 Intersubject and intrasubject variability were modeled as follows: NONLINEAR MIXED EFFECTS MODELING Cl/F, V c /F, K a and T lag are the population mean estimates for the apparent systemic clearance, the apparent volume of distribution, the absorption rate constant and the lag time, respectively. Each left-hand parameter is the corresponding true parameter for the jth subject.  1 through  6 are normally distributed random variables with mean zero and whose variance are being estimated, C is predicted drug concentration at the ith time for the jth subject, C ij is the observed concentration for that subject at that time and  ij is the normally distributed residual intrasubject error with mean zero and whose variance is being estimated.

NONLINEAR MIXED EFFECTS MODELING  The first-order estimation method was used to estimate - population PK parameters - intersubject variability (in population PK parameters) - intrasubject variability (between observations and predictions)  The pharmacostatistical model -developed by initially 1- or 2-compartment model with first-order absorption and elimination. -parameterized in terms of clearance and volume of distribution using the NONMEM subroutine ADVAN2 TRANS2 and ADVAN4 TRANS4.

NONMEM was used for nonlinear mixed effects modeling. NONLINEAR MIXED EFFECTS MODELING  Model building step Parameters were added to the model mainly based on the decrease in the minimum objective function (MOF). The change in the MOF between reduced model and full model is approximately  2 distributed with degrees of freedom equal to the number of parameters which are set to a fixed value in the reduced model. Thus, a decrease of 6 units in the MOF was considered statistically significant (p<0.01) for addition of one parameter during the development of the model. The importance of different potential covariates (age, weight, height and serum creatinine concentration) was evaluated based on changes in the MOF and inspection of scatter plots of each covariate versus the individual pharmacokinetic parameters. In addition, a decrease in unexplained variability and an improvement in plots were investigated, also.

Population Analysis Concept F: PRED F: IPRE Y Typical value of CL ( No interindividual random variability )

Fig. 1. Levosulpiride serum concentration-time plot after oral administration of a 25 ㎎ dose. ○ : mean concentration at each time. ＋ : individual serum concentrations.

Table 1. Comparison of AIC value for pharmacokinetic model evaluation for levosulpiride. Model AIC No weight1/obs a) 1/pred b) 1/(obs) 2 1/(pred) 2 A B C D a) obs :observed concentration b) pred : predicted concentration

Table 2. Coefficient of variation (CV) of parameters from fitting 1-compartment and 2-compartment model with lag time to weighted (1/(pred) 2 ) levosulpiride data. 1-compartment ParameterCV% Volume/F2.75 Ka8.90 Kel2.40 Tlag compartment ParameterCV% A51.88 B38.65 Ka21.47 Alpha Beta16.31 Tlag5

Fig. 2. Levosulpiride serum concentration-time plot. ━ : predicted concentration from fitting 1-compartment model with lag time to weighted mean data. ○ : observed mean concentration at each time. ‥ : individual concentration-time curve.

Table 3. Population pharmacokinetic parameters of levosulpiride by STS method in Korean healthy subjects. Non-compartmental parameters AUC 0 ∼ ∞ (ng∙hr/ ㎖ ) Cl/F ( ㎖ /hr) V c /F ( ㎖ ) Cmax (ng/ ㎖ ) Tmax (hr) t 1/2λz (hr) Mean S.D Compartmental parameters K a (hr -1 ) K el (hr -1 ) V c /F ( ㎖ ) Tlag (hr) Mean S.D

Fig. 3. Observed concentration versus predicted concentration plot for levosulpiride.

Fig. 4. Terbinafine serum concentration-time plot after oral administration of a 125 ㎎ dose. ○ : mean concentration at each time. ＋ : individual serum concentrations.

Table 4. Comparison of AIC value for pharmacokinetic model evaluation. Model AIC No weight1/obs a) 1/pred b) 1/(obs) 2 1/(pred) 2 C D a) obs :observed concentration b) pred : predicted concentration

Fig. 5. Terbinafine serum concentration-time plot. ━ : predicted concentration from fitting 2-compartment model with lag time to weighted mean data. ○ : observed mean concentration at each time. ‥ : individual concentration-time curve.

Table 5. Population pharmacokinetic parameters of terbinafine obtained by STS method in Korean healthy subjects.

Fig. 6. Observed concentration versus predicted concentration plot for terbinafine.

Table 6. Correlation coefficients between PK parameters and subject’s characteristics of levosulpiride. * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

Table 7. Model building steps for levosulpiride.

Table 8. Population pharmacokinetic parameter estimates of levosulpiride using NONMEM.

Fig. 7. The plots for the goodness of fit for levosulpiride. (A) Observed concentrations versus predicted concentrations scatter plot. (B) Predicted concentrations versus weighted residuals scatter plot.

Fig. 8. The plot showing the comparison of predictied levosulpiride concentrations between STS method and NONMEM. ＋ : individual levosulpiride observations. ━ : predicted concentration obtained by NONMEM. ○ : predicted mean concentration at each time by STS method.

Table 8. Correlation coefficients between PK parameters and subject’s characteristics for terbinafine.

Table 9. Model building steps for terbinafine.

Table 10. Population pharmacokinetic parameter estimates of terbinafine using NONMEM.

Fig. 9. The plots for the goodness of fit for terbinafine. (A) Observed concentrations versus predicted concentrations scatter plot. (B) Predicted concentrations versus weighted residuals scatter plot.

Fig. 10. The plot showing the comparison of predictied terbinafine concentrations between STS method and NONMEM. ＋ : individual terbinafine observations. ━ : predicted concentration obtained by NONMEM. ○ : predicted mean concentration at each time by STS method.

RESULTS AND CONCLUSIONS (1)  Nonlinear mixed effects modeling was used to fit an one-compartment model with lag time to the pooled levosulpiride data.  There were relationships between body weight and the apparent systemic clearance (r=0.294, p<0.01), body weight and the apparent volume of distribution (r=0.276,p<0.01).  In an one-compartment covariate model as built by NONMEM, population mean Cl/F, Vc/F, Ka and Tlag were ㎖ /hr, 7290  weight ㎖, 1.05 hr -1 and 0.39 hr.

RESULTS AND CONCLUSIONS (2)  Nonlinear mixed effects modeling was used to fit a two-compartment model with lag time to the pooled terbinafine data.  There were no relationship between the pharmacokinetic parameters and subject's weight, age, height and serum creatinine concentration.